Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecedented opportunity to investigate fundamental biological questions at the cellular level, such as stem cell differentiation or the discovery and characterization of rare cell types. The majority of the computational methods to analyze single-cell RNA-Seq data are implemented in R making it a natural tool to start working with single-cell transcriptomic data. Using real single-cell datasets, this course provides a step-by-step tutorial to the methodology and associated R packages for the following four main tasks: (1) normalization, (2) dimensionality reduction, (3) clustering, (4) differential expression analysis.
In Chapter 1, you will learn what single-cell RNA-Seq is and why it is a such a powerful technique. By the end of this chapter, you'll also know how to load, create, and access single-cell datasets in R.
In Chapter 2, we go over the first steps of the workflow to analyze single-cell RNA-seq data, which include quality control and normalization. These two steps should get all the technical issues and biases out of the way so that in the next chapters we can focus on the biological signal of interest.
When studying single-cell data at the cellular level, the number of dimensions is the number of genes. The goal of dimensionality reduction is to reduce the number of dimensions to a smaller number either to visualize the data in 2 dimensions or to prepare the dataset for subsequent steps like clustering.
In Chapter 4, we cluster cells with similar gene expression profiles and then perform differential expression (DE) analysis to find genes differentially expressed between known groups of cells. We then visualize DE genes with volcano plots and heatmaps.
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